
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]

def a__       (			A_,	A_,	A_,	A_="attention"   ):






  '''simple docstring'''





  __magic_name__								=			__magic_name__								=			np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]   )
  __magic_name__								=			k_tmp.reshape(k_tmp.shape[0],	k_tmp.shape[1] * k_tmp.shape[2]   )
  __magic_name__								=			np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]   )
  __magic_name__								=			o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1],	o_tmp.shape[2]   )
  __magic_name__								=			np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]   )
  __magic_name__								=			q_tmp.reshape(q_tmp.shape[0],	q_tmp.shape[1] * q_tmp.shape[2]   )
  __magic_name__								=			np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]   )
  __magic_name__								=			v_tmp.reshape(v_tmp.shape[0],	v_tmp.shape[1] * v_tmp.shape[2]   )
  return k, o, q, v

def a__       (			A_,	A_,	A_,	A_=False   ):






  '''simple docstring'''





  if split_mlp_wi:
    __magic_name__								=			params[f'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
    __magic_name__								=			params[f'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
    __magic_name__								=			(wi_a, wi_a)
  else:
    __magic_name__								=			params[f'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

  __magic_name__								=			params[f'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
  return wi, wo

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  return params[f'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]

def a__       (			A_,	*, A_,	A_,	A_ = False   ):






  '''simple docstring'''





  __magic_name__								=			traverse_util.flatten_dict(variables["""target"""]   )
  __magic_name__								=			{"""/""".join(A_   ): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  __magic_name__								=			"""encoder/encoder/mlp/wi_0/kernel""" in old
  print("""Split MLP:""",	A_   )

  __magic_name__								=			collections.OrderedDict()

  # Shared embeddings.
  __magic_name__								=			old["""token_embedder/embedding"""]

  # Encoder.
  for i in range(A_   ):
    # Block i, layer 0 (Self Attention).
    __magic_name__								=			tax_layer_norm_lookup(A_,	A_,	"""encoder""",	"""pre_attention_layer_norm"""   )
    __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			tax_attention_lookup(A_,	A_,	"""encoder""",	"""attention"""   )
    __magic_name__								=			layer_norm
    __magic_name__								=			k.T
    __magic_name__								=			o.T
    __magic_name__								=			q.T
    __magic_name__								=			v.T

    # Block i, layer 1 (MLP).
    __magic_name__								=			tax_layer_norm_lookup(A_,	A_,	"""encoder""",	"""pre_mlp_layer_norm"""   )
    __magic_name__	,       __magic_name__								=			tax_mlp_lookup(A_,	A_,	"""encoder""",	A_   )
    __magic_name__								=			layer_norm
    if split_mlp_wi:
      __magic_name__								=			wi[0].T
      __magic_name__								=			wi[1].T
    else:
      __magic_name__								=			wi.T
    __magic_name__								=			wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      __magic_name__								=			tax_relpos_bias_lookup(
          A_,	A_,	"""encoder"""   ).T

  __magic_name__								=			old["""encoder/encoder_norm/scale"""]

  if not scalable_attention:
    __magic_name__								=			tax_relpos_bias_lookup(
        A_,	0,	"""encoder"""   ).T
    __magic_name__								=			tax_relpos_bias_lookup(
        A_,	0,	"""decoder"""   ).T

  if not is_encoder_only:
    # Decoder.
    for i in range(A_   ):
      # Block i, layer 0 (Self Attention).
      __magic_name__								=			tax_layer_norm_lookup(A_,	A_,	"""decoder""",	"""pre_self_attention_layer_norm"""   )
      __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			tax_attention_lookup(A_,	A_,	"""decoder""",	"""self_attention"""   )
      __magic_name__								=			layer_norm
      __magic_name__								=			k.T
      __magic_name__								=			o.T
      __magic_name__								=			q.T
      __magic_name__								=			v.T

      # Block i, layer 1 (Cross Attention).
      __magic_name__								=			tax_layer_norm_lookup(A_,	A_,	"""decoder""",	"""pre_cross_attention_layer_norm"""   )
      __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			tax_attention_lookup(A_,	A_,	"""decoder""",	"""encoder_decoder_attention"""   )
      __magic_name__								=			layer_norm
      __magic_name__								=			k.T
      __magic_name__								=			o.T
      __magic_name__								=			q.T
      __magic_name__								=			v.T

      # Block i, layer 2 (MLP).
      __magic_name__								=			tax_layer_norm_lookup(A_,	A_,	"""decoder""",	"""pre_mlp_layer_norm"""   )
      __magic_name__	,       __magic_name__								=			tax_mlp_lookup(A_,	A_,	"""decoder""",	A_   )
      __magic_name__								=			layer_norm
      if split_mlp_wi:
        __magic_name__								=			wi[0].T
        __magic_name__								=			wi[1].T
      else:
        __magic_name__								=			wi.T
      __magic_name__								=			wo.T

      if scalable_attention:
        # convert the rel_embedding of each layer
        __magic_name__								=			tax_relpos_bias_lookup(A_,	A_,	"""decoder"""   ).T

    __magic_name__								=			old["""decoder/decoder_norm/scale"""]

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      __magic_name__								=			old["""decoder/logits_dense/kernel"""].T

  return new

def a__       (			A_,	A_   ):






  '''simple docstring'''





  __magic_name__								=			collections.OrderedDict([(k, torch.from_numpy(v.copy()   )) for (k, v) in converted_params.items()]   )

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    __magic_name__								=			state_dict["""shared.weight"""]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      __magic_name__								=			state_dict["""shared.weight"""]

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print("""Using shared word embeddings as lm_head."""   )
      __magic_name__								=			state_dict["""shared.weight"""]

  return state_dict

def a__       (			A_,	A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  __magic_name__								=			checkpoints.load_tax_checkpoint(A_   )
  __magic_name__								=			convert_tax_to_pytorch(
      A_,	num_layers=config.num_layers,	is_encoder_only=A_,	scalable_attention=A_   )
  __magic_name__								=			make_state_dict(A_,	A_   )
  model.load_state_dict(A_,	strict=A_   )

def a__       (			A_,	A_,	A_,	A_ = False,	A_ = False,	):






  '''simple docstring'''





  __magic_name__								=			MTaConfig.from_json_file(A_   )
  print(f'''Building PyTorch model from configuration: {config}'''   )
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    __magic_name__								=			UMTaEncoderModel(A_   )
  else:
    __magic_name__								=			UMTaForConditionalGeneration(A_   )

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(A_,	A_,	A_,	A_,	A_   )

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
  model.save_pretrained(A_   )

  # Verify that we can load the checkpoint.
  model.from_pretrained(A_   )
  print("""Done"""   )


if __name__ == "__main__":
    __lowerCAmelCase     :	Union[str, Any]       			=  argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
    # Required parameters
    parser.add_argument(
        '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
    )
    parser.add_argument(
        '--scalable_attention',
        action='store_true',
        help='Whether the model uses scaled attention (umt5 model)',
        default=False,
    )
    __lowerCAmelCase     :	Tuple       			=  parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path,
        args.config_file,
        args.pytorch_dump_path,
        args.is_encoder_only,
        args.scalable_attention,
    )



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]=True       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			()

    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					str       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )
    __magic_name__								=			resnets

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					int=True       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			()

    for resnet in self.resnets:
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					Dict=True       )     ->      Union[str, Any]:







    """simple docstring"""






    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					str=True       )     ->      Dict:







    """simple docstring"""






    for resnet in self.resnets:
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[
        FlaxResnetBlockaD(
            in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
    ]

    __magic_name__								=			[]

    for _ in range(self.num_layers       ):
      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.in_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.in_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					int=True       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.resnets[0](UpperCamelCase__     ,				UpperCamelCase__       )
    for attn, resnet in zip(self.attentions     ,				self.resnets[1:]       ):
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    return hidden_states

